Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for calculating a (broadband) wavelet corresponding to recorded seismic data.
Discussion of the Background
Seismic data are acquired to generate a profile (image) of the geophysical structure under the surface (subsurface). While this profile does not always provide an accurate location for oil and gas reservoirs, it suggests, to those trained in the field, the presence or absence of the oil and/or gas reservoirs and other pore fluids. Thus, providing a high-resolution image of the subsurface is an important part of the continuous process of the exploration of natural resources.
The seismic data may be acquired in various ways; for example, with land equipment, marine equipment, ocean bottom equipment, autonomous underwater equipment, or aerial equipment. The type of sensors and sources used in any of these scenarios are known in the art and thus, not repeated herein.
After the seismic data are acquired, they need to be processed to generate a better image of the subsurface. There are many known algorithms for processing the seismic data to obtain an image of the surveyed subsurface. Seismic inversion then transforms the seismic reflection data into a model of the physical layer properties of the Earth (e.g., impedance or velocity and density). If the inversion process is done properly, the calculated Earth model accurately represents the real physical properties.
One step in the inversion process is the estimation of the seismic wavelets (a definition for this term is provided later). The wavelet is estimated based on the seismic data; typically, log data from wells is used to constrain the wavelet. The seismic inversion results are strongly influenced by the quality of the estimated wavelet.
The inversion can be performed on one (full) stack or on several partial stacks simultaneously. A partial stack inversion requires the estimation of a wavelet per stack. It is common practice to estimate the wavelets for partial stack inversion for each stack independently.
The existing wavelet estimation methods are limited as they are very labor intensive, and the resulting wavelets can show large variations from stack to stack. This is in part because the wavelets are estimated independently. The process becomes particularly labor intensive for broadband wavelet estimation, either in full stack, or partial stack mode. Thus, there is a need for a new process that estimates the wavelet(s) in a more efficient way, while, at the same time honoring the underlying physics. Accordingly, it is desirable to provide systems and methods with such capabilities.